We proposed a hybrid approach using the computational and statistical resources of the Bayesian Networks to learn a network structure from a data set using 4 different algorithms and the robustness of the statistical methods present in the Structural Equation Modeling to check the goodness of fit from model over data. We built an intermediate algorithm to join the features of 'bnlearn' and 'lavaan' R packages. The Bayesian Networks structure learning algorithms used were 'HillClimbing', 'MaxMin HillClimbing', 'Restricted Maximization' and 'Tabu Search'.
Package details 


Author  Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Emerson P Cabrera, Julio C Nievola 
Date of publication  20170113 17:16:27 
Maintainer  Elias Carvalho <[email protected]> 
License  GPL3 
Version  0.2.0 
URL  https://sites.google.com/site/bnparp/ 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.